Assessment of heat stress contributing factors in the indoor environment among vulnerable populations in Klang Valley using principal component analysis (PCA)

Rising global temperatures can lead to heat waves, which in turn can pose health risks to the community. However, a notable gap remains in highlighting the primary contributing factors that amplify heat-health risk among vulnerable populations. This study aims to evaluate the precedence of heat stress contributing factors in urban and rural vulnerable populations living in hot and humid tropical regions. A comparative cross-sectional study was conducted, involving 108 respondents from urban and rural areas in Klang Valley, Malaysia, using a face-to-face interview and a validated questionnaire. Data was analyzed using the principal component analysis, categorizing factors into exposure, sensitivity, and adaptive capacity indicators. In urban areas, five principal components (PCs) explained 64.3% of variability, with primary factors being sensitivity (health morbidity, medicine intake, increased age), adaptive capacity (outdoor occupation type, lack of ceiling, longer residency duration), and exposure (lower ceiling height, increased building age). In rural, five PCs explained 71.5% of variability, with primary factors being exposure (lack of ceiling, high thermal conductivity roof material, increased building age, shorter residency duration), sensitivity (health morbidity, medicine intake, increased age), and adaptive capacity (female, non-smoking, higher BMI). The order of heat-health vulnerability indicators was sensitivity > adaptive capacity > exposure for urban areas, and exposure > sensitivity > adaptive capacity for rural areas. This study demonstrated a different pattern of leading contributors to heat stress between urban and rural vulnerable populations.


Study duration
The study was conducted between May to September 2022, coinciding with the Southwest monsoon period in Malaysia.The Southwest monsoon in Malaysia typically spans from May to September and is characterized by low precipitation, less cloud cover, high outgoing long-wave radiation, often featuring a dry period 5 .This study focused on this period due to its reputation for elevated temperatures, as previously classified as the hottest months of the year (April, May, and June) in prior research 6 .It is assumed that the temperatures during the Southwest monsoon are indicative of heatwaves conditions.This study involves three phases: Phase I: the pilot study; Phase II: screening; and Phase III: data collection for the main study.A pilot study involving 20 randomly selected participants was conducted in June 2022 to obtain the prevalence of heat stress symptoms, followed by the screening phase in July 2022 and data collection for the main study between July and September 2022.

Study participants
A comparative cross-sectional study design was used, and an equal number of respondents from urban and rural areas in the Klang Valley, Malaysia, were recruited using stratified random sampling.The calculation of the sample size using comparing two means formula 24 was based on the prevalence of heat stress symptoms reported in the pilot study for both urban and rural areas.A total of 108 respondents were recruited, achieving a 96% response rate.The study participants were selected through a screening checklist based on the inclusive criteria.Those who were classified as vulnerable in any groups (aged 60 years old and above, people with health morbidity, and people with low income) and experiencing any heat stress symptoms for the preceding months (April to June) while residing in their residential areas were included in this study.Conversely, pregnant women and individuals under 13 years old were excluded from this study due to hormonal, blood volume and circulation changes in pregnant women 25 as well as the underdevelopment of thermoregulatory mechanisms in children 26,27 , which have the potential to confound the study results.

Research tools
An adapted questionnaire from the Guidelines on Heat Stress Management at Workplace 13 was used to assess heat stress symptoms during Phases I and II.In Phase III, a self-administered questionnaire was used to obtain the participants' sociodemographic background, health status, lifestyle information, and residential information for the target population.The questionnaire underwent content validation, achieving a Scale Content Validity Index (S-CVI) value of 1.0, which is considered acceptable 28 .A pre-test was conducted during the pilot study to assess test-retest reliability.The intraclass correlation coefficient for the continuous data ranged from 0.78

Statistical analysis
The data obtained from the respondents were analyzed using Statistical Package for the Social Sciences (SPSS) version 25.A descriptive analysis was used to get the average and frequency of sociodemographic background, health status, lifestyle information, and residential information.A principal component analysis (PCA) was conducted to determine the factors contributing to heat stress.Data preprocessing, which included the encoding categorical variables and the standardizing numerical variables, was applied before the PCA analysis 33 .PCA for mixed data was employed, leveraging its powerful technique in interpreting the status of variables across different data types 34 .Only datasets with a Kaiser-Meyer-Olkin measure (KMO) value of > 0.5 35 and Barlett's test of sphericity (BTS) yielding a result of 0.000 (p < 0.001) were selected for interpretation 33 .
PCA was used for the data reduction by extracting a limited number of principal components (PCs), and varimax rotation was applied to maximize the variances of factor loadings across variables of each factor to enhance the interpretability of the result 36 .The PC variables with a factor loading of 0.4 and higher were selected as significantly loaded items as recommended for the rotated factor pattern 36 .Principal components (PCs) with eigenvalue > 1.0 were extracted for the result.A cumulative variance of at least 60% is considered acceptable 35 .Additionally, PCs with a percentage of more than 10% variance were highlighted as the main contributing factors of heat stress and were further categorized into three heat health vulnerability indicators, which are sensitivity, exposure and adaptability based on the variables' criteria 37 .Table 1 shows the variables included in the PCA analysis.

Sociodemographic background, health status, lifestyle information and residential information
Table 2 shows the sociodemographic background of the study population from urban and rural areas.A total of 108 Malaysians aged 13 years old and above were recruited in this study; 54 participants were from urban areas, Table 1..The highest education level among the study population was recorded for secondary school (68.5%), followed by no formal education (13.0%) and primary school (11.1%).Most respondents are involved in indoor-type of occupations (92.6%) such as cleaning sectors, working from home, office clerk, lorry driver, and housewife.Notably, 90.7% of the respondents were from low-income or B40 groups (which represent the bottom 40% of income in Malaysia, according to the Department of Statistics Malaysia 39 .For rural areas, the average age of the study population is 49 ± 16.1 years old, with an age range of 13 to 76 years old.The majority of participants from rural areas are female (66.7%).The average body mass index (BMI) of respondents from rural areas is 27.2 ± 6.42 kg/m 2 , which also falls in the overweight category.Most of the respondents from rural areas had the highest education level of secondary school (50.0%), followed by primary school (29.6%), and tertiary education (18.5%).Most of them are involved in indoor-type occupations (87.0%) compared to outdoor-type (13.0%),where the typical outdoor jobs among them are agriculture-based, such as farmer, gardener, and landscaper.It was recorded that 88.9% of the respondents were categorized as low-income or B40 groups.
Table 3 shows the health status and lifestyle information of the study population.Based on the result, 46.3% of the urban area respondents and 53.7% of the rural areas respondents have health morbidities such as hypertension, diabetes, obesity, respiratory diseases, cardiovascular diseases, kidney diseases, and skin diseases.More rural respondents (48.1%) were taking medicine regularly as prescribed by medical practitioners compared to the urban respondents (38.9%).The average daily water intake for urban and rural respondents was within the recommended amount by the Ministry of Health Malaysia 40 , ranging between 1.5 and 2.0 L per day.The calculated MET value categorizes most urban (53.7%) and rural (46.3%) respondents being at a moderate level of physical activity.Only 3.7% of urban and 16.7% of rural respondents smoke.None of the respondents is regular alcohol drinkers.
Table 4 shows the residential information of the study population.Most urban respondents (83.3%) live in multi-story buildings, staying for almost 17 ± 6.34 years.In contrast, most rural respondents (61.1%) live in landed houses, staying for nearly 30 ± 6.52 years.For the residential information, the average building age (year) in urban areas is 26.41 ± 10.18 and 29.70 ± 10.29 for rural.The building size (m 2 ) is relatively lower in urban areas (80.67 ± 25.37) compared to rural areas (165.82 ± 73.6).Most urban (88.9%) and rural (61.1%) houses equipped with ceilings, with an average of 3.51 ± 0.63 m and 4.26 ± 1.38 m in height, respectively.The wall material for urban residential buildings is primarily concrete (83.3%), whereas in rural areas primarily bricks with cement plaster (63.0%).Both urban and rural houses mainly used concrete as roof material.Building density and green plot ratios recorded in urban and rural areas showed that urban areas are denser with buildings and lower with green spaces than rural areas.), and PC5 (smoking [yes], lower daily water intake, and lower physical activity level).Table 6 shows the result of PCA for the rural vulnerable population.Based on PCA analysis for rural areas, 15 factors were identified as contributing factors with significant loading values ≥ 0.4), further grouped into five PCs.The five PCs with eigenvalue > 1.0 explain 71.5% of the variability from the original contributing factors.The components comprised PC1 (ceiling availability [no], higher thermal conductivity roof material, increased building age, shorter residency duration), PC2 (health morbidity [yes], medicine intake [yes], and increased age), PC3 (gender [female], higher BMI, and smoking [no]), PC4 (increased household income, higher educational level, lower daily water intake), and PC5 (higher physical activity level and occupation type [outdoor]).
Urban and rural areas have three PCs with more than 10% variances, highlighted as the primary contributing factors to heat stress.For urban, PC1 (health morbidity, medicine intake, and increased age) can be classified as sensitivity, PC2 (outdoor occupation type, lack of ceiling, and longer residency duration) as adaptive capacity, and PC3 (lower ceiling height and increased building age) as exposure.For rural areas, PC1 (lack of ceiling, higher thermal conductivity roof material, increased building age, and shorter residency duration) can be classified as exposure, PC2 (health morbidity, medicine intake, and increased age) as sensitivity, and PC3 (female, non-smoking, and increased BMI) as adaptive capacity.To summarize, the pattern of heat-health vulnerability indicators in urban areas in decreasing order according to the percentage of variances was sensitivity > adaptive capacity > exposure.In contrast, heat-health vulnerability indicators in rural areas were exposure > sensitivity > adaptive capacity.

Discussion
Based on the results, different patterns of heat stress-contributing factors were discovered between urban and rural areas in this study.This finding corresponded to a previous study that agreed heat stress-contributing factors varied in different areas 41 .A comparative study on the risk factors of heat related illnesses between urban and rural areas found that the urban population is influenced by low education levels, poverty, living in old building structures, and mobile homes with poor insulation.In contrast, risk factors in rural areas include elderly, outdoor workers in agricultural sectors, mobile homes with poor insulation, and developed land 42 .Another study indicated that urban populations are more susceptible to heat stress due to high heat-prone areas, while rural populations influenced by poor health status, poverty, and challenges in accessing healthcare related to geographic and finances 43 .While other studies classified heat stress contributing factors into several groups, commonly individual, environmental, and occupational 44,45 , our study categorizes these factors into three heat health vulnerability indicators, which are sensitivity, exposure, and adaptive capacity to enhance the appropriate mitigation measures during extreme heat events.
This study revealed that sensitivity is the most impactful indicator influencing heat-health vulnerability in urban areas (PC1), while it ranks as a second most important indicator in rural areas (PC2).Both areas www.nature.com/scientificreports/highlighted similar factors, including the presence of health morbidity, medicine intake, and older age.Individuals with health morbidities such as diabetes, obesity, hypertension, respiratory disease, and cardiovascular disease have physiological deficiencies for acclimatization 46 , which potentially influence the relationship between heat exposure and adverse health impacts 47 .Additionally, medication consumption can interfere with thermoregulation, as anticholinergics can lead to dehydration 48 .Increased age has been linked with reduced sweat output, which is associated with a decrease in epidermal blood flow during heat exposure, thereby reducing the body's ability for thermoregulation 48 .Previous studies have also agreed that poor health status and elderly are factors related to sensitivity, increasing heat-related health issues among urban populations [49][50][51] , and rural populations 52,53 .
Exposure was found to be the most impactful indicator in rural areas (PC1), while it falls in third priority in urban areas (PC3).Most of the factors listed in PCs for both areas are related to residential characteristics.However, increased building age is found to be a similar factor in both areas.Other highlighted factors in rural areas include higher thermal conductivity roof material, absence of ceiling installation, and reduced residency duration, while lower ceiling height is highlighted as exposure factor in urban areas.Other studies have also outlined residential characteristics as exposure indicator in urban 53 , and rural areas 54 .Most of the previous studies highlighted direct sources of heat such as land surface temperature 50,54 , heatwave occurrence 55 , and high ambient temperature 56 as exposure indicator.Nonetheless, some studies have also classified contributing factors such as housing characteristics as exposure indicator in urban 53 and rural areas 52 .Previous studies have agreed that the outdoor-indoor temperature varies depending on the building characteristics 57,58 .
Adaptive capacity is another indicator ranked as the second most impactful indicator in urban areas (PC2) and third (PC3) in rural areas.However, distinct factors were observed in both PCs.Factors influencing heat stress in urban areas consist of outdoor occupation type, absence of ceiling installation, and longer residency duration, all of which may expose individuals to higher heat exposure.However, long-term exposure to hot conditions may lead to acclimatization, contributing to a better adaptive mechanism 59,60 .In contrast, rural areas highlighted gender (female), higher BMI, and non-smoking as components of adaptive capacity.Females may tolerate heat more efficiently than males due to a broader range of their resting core temperature, although they have a slower sweating response compared to males 61 .Although it is common for higher BMI to increase insulation and heat retention, it may also serve as a reservoir for fluid and electrolytes, aiding in thermoregulation and maintaining hydration status during heat exposure 62 .While other studies have outlined residential and environmental-related factors such as building type 63 and green spaces or vegetation index 64,65 as components of adaptive capacity, our study proposes acclimatization-induced factors (urban) and individual factors (rural) as components of adaptive capacity.
Our study addresses previous research gaps in several ways.Firstly, this study emphasizes comparing the patterns of heat stress contributing factors between urban and rural areas.Secondly, our study focuses on vulnerable populations to enhance understanding of heat health vulnerability, as these groups are often linked to high morbidity and mortality rates.Although prior studies have provided information on heat stress contributing www.nature.com/scientificreports/factors, limited emphasis has been placed on highlighting the precedence of these factors.Furthermore, existing mitigation plans lack area-based concerns.Therefore, our findings are essential for providing baseline information to address appropriate heat mitigation measures based on priority and specific areas or populations.However, it is important to address the study's limitations.While acknowledging that a heatwave is defined by prolonged abnormally high temperatures, this study does not equate temperatures recorded during the Southwest monsoon with heat waves.Instead, we assumed that the sampling periods could reflect heat wave conditions based on the climate data mentioned in the previous study, rather than conducting heat exposure monitoring to confirm the existence of extreme heat or heat wave occurrence.Future research endeavors could explore the specific quantification of heatwave events during the Southwest monsoon period to further refine the understanding of heat stress dynamics in this region and enhance the validity of these findings.Additionally, it is worth noting that data collection was primarily conducted on weekdays, which may have limited the randomization of respondents, particularly concerning working adults and young adults attending school.

Conclusion
This study demonstrates different patterns of primary heat stress contributors among vulnerable populations in urban and rural areas.It sheds light on the unique challenges faced by both urban and rural vulnerable populations.The findings not only enhance our understanding of how specific community characteristics in urban and rural settings may influence heat stress differently, but also highlight the factors that should be prioritized to effectively address appropriate heat health adaptation and mitigation responses, reducing the adverse impacts of excessive heat exposure on vulnerable populations.

Type of factor Factors Indicators (for + loadings value)
mass index (BMI) is 28.9 ± 6.99 kg/m 2 , classified as overweight by the Ministry of Health Malaysia

Table 5
shows the result of PCA for the urban vulnerable population.Based on PCA analysis for urban areas, 14 factors were identified as contributing factors with significant loadings value (≥ 0.4), further grouped into five principal components (PCs).The five PCs with eigenvalue > 1.0 explain 64.3% of the variability from the original contributing factors.The components comprised PC1 (health morbidity [yes], medicine intake [yes],

Table 3 .
Health status and lifestyle information of the study population (N = 108).

Table 4 .
Residential information of the study population (N = 108).Green plot ratio (%) = percentage of green spaces in 16000 m 2 land area.Building density (%) = percentage of building density in 16000 m 2 land area.